OpenRaiser/Intern-Atlas huggingface dataset:
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
Venue: arXiv cs.LG 2024
Year: 2024

Mislabeled examples are ubiquitous in real-world machine learning datasets, advocating the development of techniques for automatic detection. We show that most mislabeled detection methods can be viewed as probing trained machine learning models using a few core principles. We formalize a modular framework that encompasses these methods, parameterized by only 4 building blocks, as well as a Python library that demonstrates that these principles can actually be implemented. The focus is on classifier-agnostic concepts, with an emphasis on adapting methods developed for deep learning models to non-deep classifiers for tabular data. We benchmark existing methods on (artificial) Completely At Random (NCAR) as well as (realistic) Not At Random (NNAR) labeling noise from a variety of tasks with imperfect labeling rules. This benchmark provides new insights as well as limitations of existing methods in this setup.
Generalizing Motion Planners with Mixture of Experts for Autonomous Driving
Venue: arXiv cs.RO 2024
Year: 2024

Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. These planners promise better generalizations on complicated and few-shot cases than previous methods. However, experiment results show that many of these approaches produce limited generalization abilities in planning performance due to overly complex designs or training paradigms. In this paper, we review and benchmark previous methods focusing on generalizations. The experimental results indicate that as models are appropriately scaled, many design elements become redundant. We introduce StateTransformer-2 (STR2), a scalable, decoder-only motion planner that uses a Vision Transformer (ViT) encoder and a mixture-of-experts (MoE) causal Transformer architecture. The MoE backbone addresses modality collapse and reward balancing by expert routing during training. Extensive experiments on the NuPlan dataset show that our method generalizes better than previous approaches across different test sets and closed-loop simulations. Furthermore, we assess its scalability on billions of real-world urban driving scenarios, demonstrating consistent accuracy improvements as both data and model size grow.
Enhancing Personalised Cybersecurity Guidance for Older Adults in Ireland
Venue: arXiv cs.HC 2024
Year: 2024

The term `Digital Divide' emerged in the mid-1990s, highlighting the gap between those with access to emerging information technologies and those without. This gap persists for older adults even in the 21st century. To address this, our study focused on how older adults in Ireland can feel safer online. We conducted a two-phase study. In Phase I, 58 participants used Dot Voting to identify top cyber-security priorities, including password management, privacy, and avoiding scams. This informed Phase II, where we held focus groups with 31 participants from rural and urban communities in Ireland. Researchers provided tailored advice through presentations and leaflets, followed by open discussions. Our findings show that, despite being highly aware of cyber-scams, older adults remain very concerned about them. Participants expressed hesitation about using online password managers and two-factor authentication but valued advice on privacy and tools that can help them feel more in control online.
Design of a Flexible Robot Arm for Safe Aerial Physical Interaction
Venue: arXiv cs.RO 2024
Year: 2024

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400~g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for \ac{MAV} in real environments subject to perturbations during interaction.
Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2024
Year: 2024

Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model's performance from a labeled source domain to an unlabeled target domain without direct access to source samples, addressing critical data privacy concerns. However, most existing SF-UDA approaches assume the availability of abundant source domain samples, which is often impractical due to the high cost of data annotation. To address the dual challenges of limited source data and privacy concerns, we introduce a data-efficient, CLIP-powered dual-branch network (CDBN). This architecture consists of a cross-domain feature transfer branch and a target-specific feature learning branch, leveraging high-confidence target domain samples to transfer text features of source domain categories while learning target-specific soft prompts. By fusing the outputs of both branches, our approach not only effectively transfers source domain category semantic information to the target domain but also reduces the negative impacts of noise and domain gaps during target training. Furthermore, we propose an unsupervised optimization strategy driven by accurate classification and diversity, preserving the classification capability learned from the source domain while generating more confident and diverse predictions in the target domain. CDBN achieves near state-of-the-art performance with far fewer source domain samples than existing methods across 31 transfer tasks on seven datasets.
Kaninfradet3D:A Road-side Camera-LiDAR Fusion 3D Perception Model based on Nonlinear Feature Extraction and Intrinsic Correlation
Venue: arXiv cs.CV 2024
Year: 2024

With the development of AI-assisted driving, numerous methods have emerged for ego-vehicle 3D perception tasks, but there has been limited research on roadside perception. With its ability to provide a global view and a broader sensing range, the roadside perspective is worth developing. LiDAR provides precise three-dimensional spatial information, while cameras offer semantic information. These two modalities are complementary in 3D detection. However, adding camera data does not increase accuracy in some studies since the information extraction and fusion procedure is not sufficiently reliable. Recently, Kolmogorov-Arnold Networks (KANs) have been proposed as replacements for MLPs, which are better suited for high-dimensional, complex data. Both the camera and the LiDAR provide high-dimensional information, and employing KANs should enhance the extraction of valuable features to produce better fusion outcomes. This paper proposes Kaninfradet3D, which optimizes the feature extraction and fusion modules. To extract features from complex high-dimensional data, the model's encoder and fuser modules were improved using KAN Layers. Cross-attention was applied to enhance feature fusion, and visual comparisons verified that camera features were more evenly integrated. This addressed the issue of camera features being abnormally concentrated, negatively impacting fusion. Compared to the benchmark, our approach shows improvements of +9.87 mAP and +10.64 mAP in the two viewpoints of the TUMTraf Intersection Dataset and an improvement of +1.40 mAP in the roadside end of the TUMTraf V2X Cooperative Perception Dataset. The results indicate that Kaninfradet3D can effectively fuse features, demonstrating the potential of applying KANs in roadside perception tasks.
Rethinking State Management in Actor Systems for Cloud-Native Applications
Venue: arXiv cs.DB 2024
Year: 2024

The actor model has gained increasing popularity. However, it lacks support for complex state management tasks, such as enforcing foreign key constraints and ensuring data replication consistency across actors. These are crucial properties in partitioned application designs, such as microservices. To fill this gap, we start by analyzing the key impediments in state-of-the-art actor systems. We find it difficult for developers to express complex data relationships across actors and reason about the impact of state updates on performance due to opaque state management abstractions. To solve this conundrum, we develop SmSa, a novel data management layer for actor systems, allowing developers to declare data dependencies that cut across actors, including foreign keys, data replications, and other dependencies. SmSa can transparently enforce the declared dependencies, reducing the burden on developers. Furthermore, SmSa employs novel logging and concurrency control algorithms to support transactional maintenance of data dependencies. We demonstrate SmSa can support core data management tasks where dependencies across components appear frequently without jeopardizing application logic expressiveness and performance. Our experiments show SmSa significantly reduces the logging overhead and leads to increased concurrency level, improving by up to 2X the performance of state-of-the-art deterministic scheduling approaches. As a result, SmSa will make it easier to design and implement highly partitioned and distributed applications.
TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration
Venue: arXiv cs.NE 2024
Year: 2024

Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research.
Towards Optimal Adapter Placement for Efficient Transfer Learning
Venue: arXiv cs.LG 2024
Year: 2024

Parameter-efficient transfer learning (PETL) aims to adapt pre-trained models to new downstream tasks while minimizing the number of fine-tuned parameters. Adapters, a popular approach in PETL, inject additional capacity into existing networks by incorporating low-rank projections, achieving performance comparable to full fine-tuning with significantly fewer parameters. This paper investigates the relationship between the placement of an adapter and its performance. We observe that adapter location within a network significantly impacts its effectiveness, and that the optimal placement is task-dependent. To exploit this observation, we introduce an extended search space of adapter connections, including long-range and recurrent adapters. We demonstrate that even randomly selected adapter placements from this expanded space yield improved results, and that high-performing placements often correlate with high gradient rank. Our findings reveal that a small number of strategically placed adapters can match or exceed the performance of the common baseline of adding adapters in every block, opening a new avenue for research into optimal adapter placement strategies.
Enabling Hexa-X 6G Vision: An End-to-End Architecture
Venue: arXiv cs.NI 2024
Year: 2024

The end-to-end (E2E) architecture for the 6th generation of mobile network (6G) necessitates a comprehensive design, considering emerging use cases (UCs), requirements, and key value Indicators (KVIs). These UCs collectively share stringent requirements of extreme connectivity, inclusivity, and flexibility imposed on the architecture and its enablers. Furthermore, the trustworthiness and security of the 6G architecture must be enhanced compared to previous generations, owning to the expected increase in security threats and more complex UCs that may expose new security vulnerabilities. Additionally, sustainability emerges as a critical design consideration in the 6G architecture. In light of these new set of values and requirements for 6G, this paper aims to describe an architecture proposed within the Hexa-X, the European 6G flagship project, capable of enabling the above-mentioned 6G vision for the 2030s and beyond.
Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries
Venue: arXiv cs.LG 2024
Year: 2024

Knowledge transfer in multi-task learning is typically viewed as a dichotomy; positive transfer, which improves the performance of all tasks, or negative transfer, which hinders the performance of all tasks. In this paper, we investigate the understudied problem of asymmetric task relationships, where knowledge transfer aids the learning of certain tasks while hindering the learning of others. We propose an optimisation strategy that includes additional cloned tasks named self-auxiliaries into the learning process to flexibly transfer knowledge between tasks asymmetrically. Our method can exploit asymmetric task relationships, benefiting from the positive transfer component while avoiding the negative transfer component. We demonstrate that asymmetric knowledge transfer provides substantial improvements in performance compared to existing multi-task optimisation strategies on benchmark computer vision problems.
Integer Polynomial Factorization by Recombination of Real Factors: Re-evaluating an Old Technique in Modern Era
Venue: arXiv cs.SC 2024
Year: 2024

Polynomial factorization over $ZZ$ is of great historical and practical importance. Currently, the standard technique is to factor the polynomial over finite fields first and then to lift to integers. Factorization over finite fields can be done in polynomial time using Berlekamp or Cantor-Zassenhaus algorithms. Lifting from the finite field to $ZZ$ requires a combinatorial algorithm. The van Hoeij algorithm casts the combinatorial problem as a knapsack-equivalent problem, which is then solved using lattice-reduction (the LLL algorithm) in polynomial time, which is implemented in many computer algebra systems (CAS). In this paper, we revisit the old idea of starting with factorization over $RR$ instead of a finite field, followed by recombination of the resulting linear and quadratic factors. We transform the problem into an integer subset sum problem, which is then solved using the Horowizt-Sinha algorithm. This algorithm can factor a random integer polynomial of degree $d$ in a time complexity of $O(2^(d slash 4))$. While the resulting algorithm is exponential, consistent with the integer subset sum problem being in NP, it has a few advantages. First, it is simple and easy to implement. Second, it is almost embarrassingly parallelizable. We demonstrate this by implementing the algorithm in a Graphic Processing Unit (GPU). The resulting code can factor a degree 100 polynomial is a few tenths of a second, comparable to some standard CAS. This shows that it is possible to use current hardware, especially massively parallel systems like GPU, to the benefit of symbolic algebra.
Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process
Venue: arXiv cs.CL 2024
Year: 2024

The paper considers an approach of using Google Search API and GPT-4o model for qualitative and quantitative analyses of news through retrieval-augmented generation (RAG). This approach was applied to analyze news about the 2024 US presidential election process. Different news sources for different time periods have been analyzed. Quantitative scores generated by GPT model have been analyzed using Bayesian regression to derive trend lines. The distributions found for the regression parameters allow for the analysis of uncertainty in the election process. The obtained results demonstrate that using the GPT models for news analysis, one can get informative analytics and provide key insights that can be applied in further analyses of election processes.
ATOMIC: Automatic Tool for Memristive IMPLY-based Circuit-level Simulation and Validation
Venue: arXiv cs.ET 2024
Year: 2024

Since performance improvements of computers are stagnating, new technologies and computer paradigms are hot research topics. Memristor-based In-Memory Computing is one of the promising candidates for the post-CMOS era, which comes in many flavors. Processing In memory Array (PIA) or using memory, is on of them which is a relatively new approach, and substantially different than traditional CMOS-based logic design. Consequently, there is a lack of publicly available CAD tools for memristive PIA design and evaluation. Here, we present ATOMIC: an Automatic Tool for Memristive IMPLY-based Circuit-level Simulation and Validation. Using our tool, a large portion of the simulation, evaluation, and validation process can be performed automatically, drastically reducing the development time for memristive PIA systems, in particular those using IMPLY logic. The code is available at https://github.com/fabianseiler/ATOMIC.
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
Venue: arXiv cs.LG 2024
Year: 2024

Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse policies recovering methods usually employ a vanilla behavioral cloning learning objective conditioned on the latent style, treating each state-action pair in the trajectory with equal importance. Based on an observation that in many scenarios, behavioral styles are often highly relevant with only a subset of state-action pairs, this paper presents a new principled method in diverse polices recovery. In particular, after inferring or assigning a latent style for a trajectory, we enhance the vanilla behavioral cloning by incorporating a weighting mechanism based on pointwise mutual information. This additional weighting reflects the significance of each state-action pair's contribution to learning the style, thus allowing our method to focus on state-action pairs most representative of that style. We provide theoretical justifications for our new objective, and extensive empirical evaluations confirm the effectiveness of our method in recovering diverse policies from expert data.
Findings of the Third Shared Task on Multilingual Coreference Resolution
Venue: arXiv cs.CL 2024
Year: 2024

The paper presents an overview of the third edition of the shared task on multilingual coreference resolution, held as part of the CRAC 2024 workshop. Similarly to the previous two editions, the participants were challenged to develop systems capable of identifying mentions and clustering them based on identity coreference. This year's edition took another step towards real-world application by not providing participants with gold slots for zero anaphora, increasing the task's complexity and realism. In addition, the shared task was expanded to include a more diverse set of languages, with a particular focus on historical languages. The training and evaluation data were drawn from version 1.2 of the multilingual collection of harmonized coreference resources CorefUD, encompassing 21 datasets across 15 languages. 6 systems competed in this shared task.
Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML)
Venue: arXiv cs.AI 2024
Year: 2024

With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem and operations which were followed for decades. The current landscape is where decisions are increasingly data-driven by financial institutions with an appetite for automation while mitigating risks. The segments of financial institutions which are getting heavily influenced are retail banking, wealth management, corporate banking & payment ecosystem. The solution ranges from onboarding the customers all the way fraud detection & prevention to enhancing the customer services. Financial Institutes are leap frogging with integration of Artificial Intelligence and Machine Learning in mainstream applications and enhancing operational efficiency through advanced predictive analytics, extending personalized customer experiences, and automation to minimize risk with fraud detection techniques. However, with Adoption of AI & ML, it is imperative that the financial institute also needs to address ethical and regulatory challenges, by putting in place robust governance frameworks and responsible AI practices.
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
Venue: arXiv cs.AI 2024
Year: 2024

This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intelligence (XAI) algorithms, specifically investigating how humans understand and interact with the explanations provided by these algorithms. To achieve this, we employed a multi-disciplinary approach that included state-of-the-art research methods from social sciences to measure the comprehensibility of explanations generated by a state-of-the-art lachine learning model, specifically the Gradient Boosting Classifier (XGBClassifier). We conducted an extensive empirical user study involving interviews with 39 participants from three different groups, each with varying expertise in data science, data visualization, and domain-specific knowledge related to the dataset used for training the machine learning model. Participants were asked a series of questions to assess their understanding of the model's explanations. To ensure replicability, we built the model using a publicly available dataset from the UC Irvine Machine Learning Repository, focusing on edible and non-edible mushrooms. Our findings reveal limitations in existing XAI methods and confirm the need for new design principles and evaluation techniques that address the specific information needs and user perspectives of different classes of AI stakeholders. We believe that the results of our research and the cross-disciplinary methodology we developed can be successfully adapted to various data types and user profiles, thus promoting dialogue and address opportunities in HCAI research. To support this, we are making the data resulting from our study publicly available.